Individuals and entities increasingly share, access, and disseminate high volumes of electronic information. In addition, the ubiquity of high-speed Internet access, mobile devices, and portable storage has led to an increasingly mobile workforce. As a consequence, it has become more difficult than ever for organizations to prevent sensitive information from being lost and/or compromised.
For example, users may maliciously or unintentionally leak confidential data to unauthorized third parties via a variety of communication channels. In order to prevent these security breaches, conventional data loss prevention (DLP) systems typically monitor the flow of information over data-distribution channels. For example, many traditional DLP systems analyze distributed data by searching for particular combinations of attributes that indicate DLP threats.
Unfortunately, conventional DLP systems may have a limited number and/or a predefined set of policies and therefore may only check distributed data for a limited number of attribute combinations. In addition, these DLP policies may be broad, rather than tailored to specific DLP threats within individual organizations. These deficiencies may be enhanced as organizations grow and their communication channels diversify. Similarly, the data distribution patterns of an organization may evolve over time as the communication needs of the organization change and/or as malicious users learn to evade existing DLP policies. Conventional DLP systems, however, may be static and unable to adapt to new DLP threats. As such, the current disclosure identifies and addresses a need for more efficient and effective systems and methods for optimizing DLP systems.